How to identify the areas to reduce over fitting?
It is a difficult task to identify the areas to be corrected to improve accuracy unless that area is looking into your face. By that I mean unless the regularizing parameter has unusually large or small values it is difficult to pin down and identify which regularizer to tweak. You need to do a grid search or random search over the hyper-parameter space to come up with an optimal combination of hyper parameters. 

There could be a lot of things that you could consider to reduce over fitting. Some of them are $L_2$ regularizers, Dropout, depth of the network, number of neurons in the layers, the optimizer,  etc.